In the field of information technology, the term “biometric system” typically refers to an automated system that uses measurable physiological features or behavioral characteristics of an individual to either determine or verify the identity of the individual. Physiological biometric features that are commonly used in biometric systems include fingerprints, the face, and various features of the eye. Behavioral biometrics characteristics that are commonly used in biometric systems include voice, keystrokes, and signatures. Hereinafter, only systems that use physiological features will be discussed.
In general, biometric identification involves first capturing a sample of a physiological feature from an individual. Capturing typically involves some form of optical scanning of the physiological feature. Next, distinctive characteristics are identified in the captured sample and a mathematical representation of the distinctive features (an enrollment template) is generated. The enrollment template is then stored for later comparison.
When the identity of an individual is being determined or verified, a sample of the physiological feature is again captured from the individual. The distinctive characteristics of the captured sample are identified and a “verification template” is generated. The verification template is then compared to one or more enrollment templates stored in a database.
In the case of identity determination, the verification template is compared to a number of previously stored templates. This process is commonly referred to as a one-to-many comparison. For each template comparison, a score is determined that indicates how well the verification template matches the enrollment template. The enrollment template with the maximum score is designated as matching the verification template, and the identity of the individual will be identified as the individual associated with the enrollment template.
In the case of identity verification, the verification template is compared to the individual's previously stored enrollment template. This process is commonly referred to as a one-to-one comparison. A score is then determined that indicates how well the verification template matches the enrollment template. If the score of a comparison exceeds a given match threshold, a match will be determined to have been made and the identity of the individual will be indicated as verified. Hereinafter, only identity verification will be discussed.
In creating a biometric identity verification system there are a number of trade-offs that must be considered. For example, a trade-off is typically made between the precision and/or security of a system versus the cost of the system. For example, a system designer may choose a high-performance computing device, a large database capable of storing many templates, advanced scanning devices, a number of scanning devices at a number of different access points, and/or a highly secure network to interconnect each of these elements. While these elements may enhance the precision and/or security of the biometric identity verification system, they will also greatly increase the systems cost. Conversely, if a system designer chooses to select lower performance and/or lower precision elements, the precision and/or security of a system will be reduced.
Another trade-off is typically made between the number of false negatives that a system will generate and the number of false positives a system will generate. In general, a false positive occurs when a verification template and an enrollment template are determined to match when, in fact, the individual from whom the verification template was generated is not the same individual from whom the enrollment template was generated. In contrast, a false negative occurs when a verification template and an enrollment template are determined not to match, when in fact the verification and enrollment templates were generated from the same individual.
Typically, false positives can be reduced by generating extremely detailed verification and enrollment templates and by setting the match threshold high. Unfortunately, a system that creates very detailed templates will typically be more expensive and/or computationally complex than a system that creates less detailed templates. Additionally, a system that uses a very high match threshold will typically generate more false negatives. Conversely, a system that uses a low match threshold will typically generate more false positives.